Projets novateurs réalisés

Fault-Seal Analysis at Aquistore

Faulting might be affecting the flow of injected CO2 into the subsurface at the Aquistore Carbon Capture and Storage facility in southeastern Saskatchewan. 3D seismic data indicate that faulting affects the Deadwood Sandstone injection target approximately 100 m to the west of the injection well. The seismic data cannot however image all the structural complexity that is likely to be associated with the faulting. As such, predicting how or if that faulting affects the movement of CO2 is problematic; the fault could act as a conduit or a barrier.

This project supports a masters student in Earth Sciences at Western University. The student will adapt a seismic- and outcrop-based workflow, known as fault-seal analysis, that is used in the petroleum industry predict the effects of faults on the movement of gases and fluids. The product will be a 3D model of the stratigraphy and structure near the injector well. The model can be employed by reservoir engineers to simulate the movement of CO2, thereby allowing PTRC and its associates to make improved management decisions.

Modelling pathways of dredged sediment placed on Sturgeon Bank

Sturgeon Bank is a Wildlife Management Area within the unceded traditional territories of the Coast Salish peoples, and west of the City of Richmond, British Columbia. Sturgeon Bank is primarily comprised of intertidal marsh, mud flats and open water, and provides several ecological and coastal risk mitigation benefits. However, monitoring indicates that Sturgeon Bank’s salt marsh areas have receded by more than 30% since 1986. The Sturgeon Bank Sediment Enhancement Pilot Project (SBSEPP) is a field level pilot project designed in part by the Mitacs partner organization to address the observed marsh recession. The SBSEPP involves the placement of sediments on the mudflats, with the premise that the sediments will be transported onshore by waves and tidal circulation, supporting marsh restoration. Beginning in 2021, three sediment deposits have been completed at Sturgeon Bank, and large quantities of data have been collected to monitor the site’s progression. The Mitacs intern will explore the collected data using data analysis and numerical modelling software to provide the Mitacs partner organization with insight into the sediment transport processes observed in the SBSEPP. Such insight is crucial in potential upscaling of project components for marsh restoration throughout the Fraser Delta and throughout Canada at large.

Advancing Mass Timber Durability through Hygrothermal Field Measurements and Predictive Modelling

This project aims to make mass timber buildings, a sustainable alternative to concrete and steel, even more reliable. While mass timber is great for the environment, we don’t fully understand how it handles moisture over time, which can affect its durability. Our project will tackle this by using advanced sensors to monitor a new mass timber building under construction in Toronto, collecting real-world data on how the wood reacts to moisture during construction and one people occupy the building. This will help our partner, Synergy Partners Consulting Ltd., a leading building science company, to better predict and manage moisture risks. The outcome will be more durable and sustainable mass timber buildings, strengthening Canada’s leadership in green construction and ensuring healthier environments for everyone.

Conceptual design and dynamics modelling of tethered solar shield

The objective of the proposed project is the development of a tethered solar shield to reduce the amount of solar radiation to Earth, thereby opening up the possibility to combat global warming. The intern will work on the following two phases. The intern will begin by carrying out literature review on the designs proposed for solar shields to date, and tethered systems deployed in the solar system. The intern will carry out basic analysis to determine the mass and tether length requirements on the shield and the ballast body. Once the basic system design has been established, the intern will work on formulating the dynamics model of this system. Different modeling options will be considered for the shield, the ballast body and tether, ranging from point mass to flexible-body models. The relevant perturbations that are important for the particular system will be assessed and suitable models for those perturbations will be identified. The formulated model will be implemented in Matlab and dynamics analysis will be carried out to study the response of the system. This work will provide the necessary insights into the requirements for the next phase of the project which is the control of the tethered solar shield.

Adaptive OCR for Structured Data Extraction in Technical PDF Documents

The proposed research project aims to digitize information currently locked in image-only PDF files, which are common in government and engineering sectors but are difficult to search and analyze. These documents, which often include technical drawings, forms, and reports, cause inefficiencies and errors due to the need for manual review. To address this, the project will develop an adaptive system using advanced image recognition (OCR) and vision-language models to accurately extract structured data. This involves creating algorithms for content and layout analysis and building a user-friendly software tool to help people upload, view, and export this extracted information. For the participating institutions, the University of Alberta in Canada and the National Taiwan University of Science and Technology, this project strengthens their ongoing collaboration in digitalizing building processes and advances data-driven design and construction. The project’s outcomes will streamline document workflows, reduce manual effort, and improve data analysis for both academic research and real-world applications in engineering and government.

From Agents to Teams: Optimizing the Creation of LLM-based Multi-Agent Systems

As multi-agent systems become more complex, improving how Large Language Model (LLM)-based agents work together is increasingly important. This research focuses on creating reusable design patterns and adaptive coordination strategies to boost collaboration and system performance. Blend360 is an AI service provider that builds advanced solutions for clients in sectors like finance, healthcare, and consumer goods. The company is exploring the use of multi-agent systems in its AI toolkit. These systems break down client problems into smaller tasks that multiple AI agents can solve together. However, there are key challenges preventing their smooth deployment in real-world applications. First, current systems often use static workflows (such as sequential or hierarchical setups), which limit flexibility in dynamic environments. Second, there is a lack of reusable design patterns to guide how agents interact, making these systems hard to build and maintain. Third, existing evaluation methods focus only on whether a task is completed, ignoring how well agents collaborate, how much each contributes, or how efficient their communication is. This research will help address these issues and directly support the development of smarter, more scalable agentic AI tools for Blend360’s clients.

Cartographier l’écosysème bioalimentaire du Saguenay–Lac-Saint-Jean

Le projet vise à développer un portrait clair et actuel des organismes qui soutiennent le développement du secteur agroalimentaire au Saguenay–Lac-Saint-Jean. Bien que la région soit riche en ressources, en expertises et en initiatives, il est parfois difficile pour les entreprises de savoir à quelle porte frapper pour obtenir du soutien. Plusieurs organismes travaillent fort pour aider les producteurs, transformateurs ou entrepreneurs, mais un manque de coordination rend les démarches parfois complexes ou inefficaces. Ce projet vise à cartographier l’écosystème bioalimentaire régional : identifier qui fait quoi, où, avec qui, et dans quels domaines. En dressant ce portrait détaillé des organisations et de leurs liens, on pourra repérer les doublons, les manques, mais aussi les occasions de collaboration. Cela permettra aux organismes de mieux orienter les entrepreneur·e·s, de renforcer la complémentarité entre les services offerts et d’augmenter l’impact des actions menées dans la région. À long terme, ce projet aidera à bâtir un système alimentaire plus cohérent, plus inclusif et plus durable pour toute la population du Saguenay–Lac-Saint-Jean.

Global Greenhouse Agricultural Supply Chain Design and Optimization under Trade Policy and Market Uncertainty

Global agricultural supply chains face persistent challenges such as unstable supply, uncertain demand, and tariff-related uncertainty that further complicates pricing and network design. This project focuses on the design and optimization of global greenhouse agricultural supply networks under trade policy and market uncertainty. The research aims to improve supply chain profitability, resilience, and adaptability to changing trade and market conditions. By leveraging the expertise of both participating institutions, the project will generate new models and solution methods applicable to a broad range of agricultural and network design problems. Outcomes will include joint publications, enhanced research capabilities, and a stronger foundation for long-term collaboration through shared expertise, future funding proposals, and the exchange of students and researchers. This collaboration will contribute both theoretical advancements and practical decision-support tools for improving global agricultural supply chains.

Quantification of Cerebrospinal Fluid and Plasma Amyloid-ß 34, 37, and 38: Correlation with Positron Emission Tomography Imaging and Alzheimer’s Disease Progression

This project focuses on improving how we detect Alzheimer’s disease early by measuring lesser-known, protective forms of the amyloid-ß protein—Aß34, Aß37, and Aß38—in blood and cerebrospinal fluid. These shorter forms are thought to help clear the harmful ones that build up in the brain and cause damage. Using a highly sensitive method called single molecule array, I’ll measure these proteins in samples from the TRIAD cohort and compare them with existing Alzheimer’s markers and brain imaging data. The aim is to develop a simple and reliable toolkit for early diagnosis, which could support both research and clinical teams at the participating institutions in identifying the disease sooner and more accurately.

Analyzing and Detecting Runway Incursions Using High-Resolution ADS-B and Incident Databases

This study focuses on enhancing aviation safety by detecting and analyzing runway incursions—unauthorized entries of aircraft, vehicles, or personnel onto active runways—using high-resolution ADS-B data from Flightradar24 (FR24). Despite existing protocols, such incursions remain a significant threat to air traffic operations. The project aims to develop and validate algorithms capable of identifying critical events, such as simultaneous runway occupancy and near-misses, by leveraging FR24’s historical ADS-B data. These data, sampled every six seconds during ground phases and every thirty seconds during cruise, will be cross-referenced with known incidents from IATA’s ACC_INC database and additional datasets from the FAA and IATA. The study will also examine contributing factors like airport geometry, weather, and runway conditions. Each incursion will be categorized by severity—from high-risk (Category A) to low-impact (Category D)—to identify patterns and risks over the past two years. In partnership with IATA, the project seeks to enhance early risk detection and situational awareness using scalable, data-driven tools. The outcomes will inform global safety standards, regulatory initiatives, and best practices. A final report, scheduled for 2026, will provide actionable recommendations aimed at reducing the frequency and severity of runway incursions worldwide.

Optimization of gene editing tools into human organoid models

Organoids are powerful cellular models that can advance our understanding of human biology and disease progression. They can be generated from human patient samples and be used to screen the best treatment course for patients, thereby allowing for a personalized therapeutic approach. However, a major challenge facing the organoid field is the lack of standard, optimized protocols for many experimental workflows. Without streamlined approaches adapted specifically for organoids, research progress and utilization of organoid models will be stalled. The objective of this project is to create standardized, open-source protocols for efficient genetic editing of different types of human organoid models. The interns funded through this proposal will work closely with scientists within the partner organization, the Children’s Health Research Institute, who have developed these protocols but have not yet standardized them across different human organoid models. The interns will generate these genetically edited human organoid models and then validate protocols by studying cellular signaling within modified human organoids. This project will enable researchers within the partner organization to overcome technical barriers associated with using human organoid models, generate high-quality data, and expand the use of human organoids to investigate complex cellular behaviors and disease mechanisms.

Optimizing Sustainability Through Value Chain Simulation

This project develops a simulation-based framework to evaluate and optimize the sustainability performance of university value chains, supporting higher education institutions in their transition to net-zero carbon emissions. The primary goal is to systematically identify and prioritize key emission sources and impactful activities within university operations. A comprehensive literature review will inform the analysis of primary and support activities, enabling the identification of significant emission sources and potential intervention points. Utilizing system dynamics software like AnyLogic, we will simulate various sustainability strategies, integrating environmental, social, and economic metrics to provide a holistic performance view. Thorough data analysis of simulation results will uncover the most effective strategies for enhancing sustainability. The findings will be synthesized to categorize these strategies based on performance indicators, yielding actionable recommendations for decision-makers. By establishing a robust framework for evaluating sustainability in university value chains, this project aims to advance the integration of sustainability in higher education planning and operations, ultimately contributing to a sustainable future and reducing the carbon footprint of educational institutions.